Eric C Rouchka, Robert M Flight and Ramin HomayouniMeeting abstracts - A single PDF containing all abstracts in this Supplement is available here.http://www.biomedcentral.com/content/pdf/1471-2105-12-S7-info.pdf

10th Annual UT-ORNL-KBRIN Bioinformatics Summit 2011

Memphis, TN, USA1-3 April 2011http://citg.uthsc.edu/summit_11/program.html1471-2105201112Suppl 7A7http://www.biomedcentral.com/1471-2105/12/S7/A710.1186/1471-2105-12-S7-A75820112011Phan et al; licensee BioMed Central Ltd.This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background

In microarray experiments involving multiple treatments, pairwise comparisons between all pairs of treatments are desirable but expensive. To cope with this, we previously introduced a method that performed all pairwise comparisons in a post hoc manner. This method employs directed graphs to represent gene response to pairs of treatments. It has been applied and found useful in identifying and differentiating genes sharing similar functional pathways 12.

Results

mDAG is a web-based software based on this method. mDAG allows users to upload microarray data in GCT format through a web interface. From this data, the application performs calculations to assign graphical patterns to genes and outputs images and textual data for further analyses. These graphical patterns carry specific meanings in terms of how genes respond to pairs of treatments. The application is implemented using Python and web2py.

mDAG is available at http://cetus.cs.memphis.edu:8080/mDAG.

Conclusion

For experiments involved multiple treatments and replicates, mDAG allows researchers to analyze and visualize in graphical representations relationships of gene interactions to all pairs of treatments. The software can be used online or off-line.

Acknowledgements

This software is supported by the Center for Alternatives to Animal Testing at the John Hopkins school of Public Heath, Project CAAT-2011-18.

Analyzing microarray data with transitive directed acyclic graphs

PhanVGeorgeEOTranQTGoodwinSBoddreddigariSSutterTRJournal of Bioinformatics and Computational Biology20097113515610.1142/S0219720009003972